RFID Indoor Localization Algorithm Based on PSO

2012 ◽  
Vol 241-244 ◽  
pp. 972-975 ◽  
Author(s):  
Pei Zhi Wen ◽  
Ting Ting Su ◽  
Li Fang Li
Keyword(s):  
Indoor Environment ◽  
Lower Cost ◽  
Indoor Localization ◽  
Main Idea ◽  
Pso Algorithm ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Smoothing Filter ◽  
The Impact

In order to improve the positioning accuracy and reduce the localization cost, a kind of PSO-based RFID indoor localization algorithm is proposed in this paper. The main idea of this algorithm contains the following two aspects. First, due to the influence of none line of sight and multipath transmission in indoor environment, we adopt Gaussian Smoothing Filter to process Received Signal Strength Indicator (RSSI) values, which can reduce the impact of environmental factors on the position estimation effectively. Second, Particle of Swarm Optimization (PSO) algorithm is introduced to obtain a better positioning result. By experimenting in different indoor environment, the results demonstrate that the proposed approach can not only improve the precision of indoor localization, but has a lower cost and better robustness when compared to VIRE approach.

scholarly journals Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6972
Author(s):  
Harun Jamil ◽  
Faiza Qayyum ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Sensor Data ◽  
Compensation Mechanism ◽  
Localization Algorithm ◽  
High Position ◽  
Zero Velocity ◽  
Conflicting Information

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

scholarly journals Indoor Localization Using Bayesian Filter

2019 ◽  
Author(s):  
Karthik Muthineni ◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Communication Technology ◽  
Indoor Localization ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Ambient Intelligent ◽  
Unknown Node ◽  
Visualization Of Data ◽  
Log Normal ◽  
Environment Parameters

Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on user and environment data obtained by sensors. Such networks are developed to give environmental monitoring and indoor localization services. In this work, Zigbee which is a wireless communication technology is used for localization based on Received Signal Strength Indicator (RSSI) method. In practice, Extended Kalman Filter (EKF) is adapted to filter RSSI values influenced by multi-path fading and noise. Log-Normal Shadowing Method (LNSM) together with the Trilateration method was implemented to locate the position of the unknown node or entity. In addition, Cramer Rao Lower Bound (CRLB) is derived for the position estimation, that can be used to evaluate the performance of the system in terms of localization accuracy. Along with indoor localization, the deployed WSN could also monitor environment parameters like temperature and humidity surrounding entity using Digital Humidity and Temperature (DHT11) sensor. Using Zigbee location coordinates of entity and environment parameters are transmitted to remote desktop where visualization of data is done using Matrix Laboratory (MATLAB).

scholarly journals NNT: Nearest Neighbour Trapezoid Algorithm for IoT WLAN Smart Indoor Localization Leveraging RSSI Height Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wilford Arigye ◽  
Mu Zhou ◽  
Muhammad Junaid Tahir ◽  
Waqas Khalid ◽  
Qiaolin Pu
Keyword(s):  
Indoor Localization ◽  
Path Loss ◽  
Cost Effective ◽  
Target Space ◽  
Nearest Neighbour ◽  
Complex Environments ◽  
Received Signal Strength Indicator ◽  
Efficient Scheme ◽  
Cell Tower ◽  
The Impact

Indoor localization as a technique for assisting, or replacing outdoor satellite and cell tower localization systems, has taken a toll in the recent Internet of Things (IoT) era. This IoT drive has prompted increased research towards indoor localization, where fingerprinting, radio mapping as a cost-effective and efficient scheme, is emerging as the best enterprise entrepreneurs choose. However, indoor complex environments comprise of trackable devices (TD) at various heights, such as child trackers, dog tags, TD on the table, TD’s in the pockets, and situations such as pedestrians talking on the phone: that is at the height of the ear, amongst others. This paper first investigates and analyses “experimentally” the impact of received signal strength indicator (RSSI) fingerprinting height to construct radio maps for indoor localization. Secondly, it proposes the novel trapezoid path loss model for RSSI estimation and finally the nearest neighbour trapezoid (NNT) algorithm for IoT smart indoor localization leveraging and mitigating the impact of height considered during the offline signal fingerprinting. We further propose approximately 1 meter above the flooring of the target space as the effective fingerprinting height for indoor localization approaches.

scholarly journals An Indoor Robust Localization Algorithm Based on Data Association Technique

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6598
Author(s):  
Long Cheng ◽  
Yong Wang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Hypothesis Testing ◽  
Indoor Localization ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Position Information ◽  
Position Estimate

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

Range-only fuzzy Voronoi-enhanced localization of mobile robots in wireless sensor networks

Robotica ◽  
2011 ◽  
Vol 30 (7) ◽  
pp. 1063-1077 ◽  
Author(s):  
D. Herrero ◽  
H. Martínez
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Fault Tolerant ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Uncertain Information ◽  
Frequency Signal ◽  
Localization Approach ◽  
Different Sources ◽  
High Uncertainty

SUMMARYWireless Sensor Network (WSN) localization has shown a growing research interest, thanks to the expected proliferation of WSN applications. This work is focused on indoor localization of a mobile robot in a WSN using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. These measurements are affected by different sources of uncertainty that make them highly noisy and unreliable. The proposed approach makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the position estimation is enhanced using a rough description of indoor environment. The experiments show that the proposed localization approach (i) is fault-tolerant, (ii) results feasible in low-density WSNs, and (iii) provides better position estimations than well-known localization methods when the position measurements are affected by high uncertainty.

scholarly journals Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI

2021 ◽  
Vol 7 (1) ◽  
pp. 40-53
Author(s):  
Dwi Joko Suroso ◽  
Farid Yuli Martin Adiyatma ◽  
Ahmad Eko Kurniawan ◽  
Panarat Cherntanomwong
Keyword(s):  
Approximation Method ◽  
Indoor Localization ◽  
Classical Method ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Performance Comparison ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Simple Power ◽  
Distance Parameter

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.

Indoor localization algorithm based on behavior-driven predictive learning in crowdsourced Wi-Fi environments

2019 ◽  
Vol 33 (14n15) ◽  
pp. 1940036 ◽  
Author(s):  
Boney A. Labinghisa ◽  
Dong Myung Lee
Keyword(s):  
Shortest Path ◽  
Indoor Localization ◽  
Principal Component ◽  
Reference Points ◽  
Average Error ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Predictive Learning ◽  
Starting Location ◽  
Error Distance

The indoor localization algorithm based on the behavior-driven predictive learning (BDPLA) executes machine-learning predictions by computing the shortest path from a starting location to a destination. The proposed algorithm selects a set of reference points (RPs) to predict the shortest path using all available RPs from the crowdsourced Wi-Fi environment. In addition, the proposed algorithm utilizes the collected received signal strength indicator (RSSI) values to determine the error distance. Using principal component analysis (PCA), the existing crowdsourced RSSI data can be calibrated to help decrease the inconsistent RSSI values among all received signals by reconstructing the values. The average error distance of 3.68 m achieved better results compared with the traditional fingerprint map with an average result of 6.96 m.

Localization Algorithm for Three-Dimensional Space of Building Based on ZigBee Wireless Networks

2014 ◽  
Vol 926-930 ◽  
pp. 1546-1549
Author(s):  
Yun Hong Liu ◽  
Tong Wei Niu
Keyword(s):  
Wireless Networks ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Signal Strength ◽  
High Accuracy ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Spatial Environment ◽  
The Impact ◽  
Three Dimensional Space

ZigBee wireless networks can be used for locating the objects monitored inside the building, in which the location method based on RSSI (Received Signal Strength Indicator) has a broad range of application. For the complex spatial environment of the building, a three-dimensional localization algorithm will be proposed in this paper which can achieve high accuracy of location, meanwhile mitigate the impact of the RSSI error on the location results efficiently. This method is applicable to the NLOS environment in condition of multi-barrier.

scholarly journals Accurate Indoor Localization Based on CSI and Visibility Graph

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2549 ◽  
Author(s):  
Zhefu Wu ◽  
Lei Jiang ◽  
Zhuangzhuang Jiang ◽  
Bin Chen ◽  
Kai Liu ◽  
...  
Keyword(s):  
Real World ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Visibility Graph ◽  
Localization Algorithm ◽  
Channel State ◽  
Critical Areas ◽  
Measuring Devices ◽  
Real World Applications ◽  
Better Than

Passive indoor localization techniques can have many important applications. They are nonintrusive and do not require users carrying measuring devices. Therefore, indoor localization techniques are widely used in many critical areas, such as security, logistics, healthcare, etc. However, because of the unpredictable indoor environment dynamics, the existing nonintrusive indoor localization techniques can be quite inaccurate, which greatly limits their real-world applications. To address those problems, in this work, we develop a channel state information (CSI) based indoor localization technique. Unlike the existing methods, we employ both the intra-subcarrier statistics features and the inter-subcarrier network features. Specifically, we make the following contributions: (1) we design a novel passive indoor localization algorithm which combines the statistics and network features; (2) we modify the visibility graph (VG) technique to build complex networks for the indoor localization applications; and (3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve about 96% accuracy on average and is more than 9% better than the state-of-the-art techniques.

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